J
Jessi Cisewski-Kehe
Researcher at Yale University
Publications - 34
Citations - 371
Jessi Cisewski-Kehe is an academic researcher from Yale University. The author has contributed to research in topics: Topological data analysis & Stars. The author has an hindex of 10, co-authored 31 publications receiving 247 citations. Previous affiliations of Jessi Cisewski-Kehe include University of Wisconsin-Madison.
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Functional summaries of persistence diagrams
TL;DR: In this paper, a unified framework for the functional summaries of persistence landscape functions is proposed, and the performance of these summaries is evaluated in the context of classification using simulated prostate cancer histology data.
Journal ArticleDOI
Finding cosmic voids and filament loops using topological data analysis
TL;DR: This method provides a novel, statistically rigorous approach for locating informative generators in cosmological datasets, which may be useful for providing complementarycosmological constraints on the effects of, for example, the sum of the neutrino masses.
Journal ArticleDOI
Finding cosmic voids and filament loops using topological data analysis
TL;DR: The Significant Cosmic Holes in Universe (SCHU) method as mentioned in this paper identifies cosmic voids and loops of filaments in cosmological datasets and assigns their statistical significance using techniques from topological data analysis.
Posted Content
The Role of Machine Learning in the Next Decade of Cosmology
Michelle Ntampaka,Camille Avestruz,Steven Boada,João Caldeira,Jessi Cisewski-Kehe,Rosanne Di Stefano,Cora Dvorkin,August E. Evrard,Arya Farahi,D. P. Finkbeiner,Shy Genel,Alyssa A. Goodman,Andy D. Goulding,Shirley Ho,Arthur Kosowsky,Paul La Plante,François Lanusse,Michelle Lochner,Rachel Mandelbaum,Daisuke Nagai,Jeffrey A. Newman,Brian Nord,J. E. G. Peek,Austin Peel,Barnabás Póczos,Markus Michael Rau,Aneta Siemiginowska,Danica J. Sutherland,Hy Trac,Benjamin D. Wandelt +29 more
TL;DR: The next decade will bring new opportunities for data-driven cosmological discovery, but will also present new challenges for adopting ML methodologies and understanding the results.
Posted Content
Functional Summaries of Persistence Diagrams
TL;DR: The definition of persistence landscape functions is generalized, several theoretical properties of the persistence functional summaries are established, and their performance in the context of classification using simulated prostate cancer histology data is demonstrated.